water Article Source Apportionment of Annual Water Pollution Loads in River Basins by Remote-Sensed Land Cover Classification Yi Wang 1, Bin He 2,*, Weili Duan 2,*, Weihong Li 1, Pingping Luo 3,4 and Bam H. N. Razafindrabe 5 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; [email protected] (Y.W.); [email protected] (W.L.) 2 Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China 3 Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region (Chang’an University), Ministry of Education, Xi’an 710064, China; [email protected] 4 School of Environmental Science and Engineering, Chang’an University, Xi’an 710064, China 5 Faculty of Agriculture, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan; [email protected] * Correspondence: [email protected] (B.H.), [email protected] (W.D.); Tel.: +86-025-8688-2171 (B.H.); +86-025-8688-2173 (W.D.) Academic Editor: Y. Jun Xu Received: 4 April 2016; Accepted: 9 August 2016; Published: 23 August 2016 Abstract: In this study, in order to determine the efficiency of estimating annual water pollution loads from remote-sensed land cover classification and ground-observed hydrological data, an empirical model was investigated. Remote sensing data imagery from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer were applied to an 11 year (1994–2004) water quality dataset for 30 different rivers in Japan. Six water quality indicators—total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and dissolved oxygen (DO)—were examined by using the observed river water quality data and generated land cover map. The TN, TP, BOD, COD, and DO loads were estimated for the 30 river basins using the empirical model. Calibration (1994–1999) and validation (2000–2004) results showed that the proposed simulation technique was useful for predicting water pollution loads in the river basins. We found that vegetation land cover had a larger impact on TP export into all rivers. Urban areas had a very small impact on DO export into rivers, but a relatively large impact on BOD and TN export. The results indicate that the application of land cover data generated from the remote-sensed imagery could give a useful interpretation about the river water quality. Keywords: Japan; river; water quality; remote sensing; AVHRR 1. Introduction Changing land use and land management practices are regarded as amongst the most important factors that can alter hydrological systems and water quality, which have become increasingly important to catchment stakeholders, such as management groups, land owners and government departments [1–4]. Generally, a river’s water quality is linked to the land cover in the watershed and is degraded by changes in the land cover patterns on their watersheds as human activities increase [5–8]. Different studies have increasingly recognized that human action at the landscape scale is a principal threat to the ecological integrity of river ecosystems and water quality [9–15]. However, the information on the sources of pollutants in catchments and on the response of water quality to changing land Water 2016, 8, 361; doi:10.3390/w8090361 www.mdpi.com/journal/water Water 2016, 8, 361 2 of 14 use practices is still limited in many catchments [16–20]. It is therefore important to understand the relationships between catchment characteristics and river water chemistry, which provides a base for determining how future changes in land cover and use and climate will impact on river water quality. In Japan, the water quality of rivers has been improved by legislation on sewage, drainage, etc. However, the water quality cannot be said to have been restored to its natural conditions because the pollution from non-point sources can increase even when factory drainage and domestic drainage are improved by law [21]. Therefore, it is still necessary to take prevention measures to control non-point source pollutants for maintaining the natural ecosystem and environment [22,23]. Among these, identification of the water pollution loads from different sources is important. To this end, statistic methods and hydrological modeling have been proposed and applied to estimate the contribution from different water pollution sources. By using multivariate statistical techniques, Shrestha and Kazama [24] evaluated the temporal/spatial variations in the Fuji river basin, illustrating the usefulness of multivariate statistical techniques for identifying pollution sources/factors and understanding temporal/spatial variation. Duan et al. (2015) developed a SPARROW-based (SPAtially Referenced Regression on Watershed Attributes) watershed model to estimate the sources and transport of suspended sediments in surface waters of the Ishikari River basin [25]. Recently, remote sensing was used to evaluating water quality. Oki and Yasuoka [26] mapped the potential annual total nitrogen load in the river basins of Japan with remotely sensed imagery of 2006. However, the non-point source pollutants are transported to water body is still unclear. Therefore, in a previous study we have analyzed the relationship between land cover types and potential annual water pollution loads and improved an empirical model to successfully calculate potential annual water pollution loads in 30 river basins in Japan by using the collected dataset in the year of 1996 [27]. Based on these previous results of estimating total water pollution loads in year 1996 for 30 river basins, the objectives of the present study are to (1) test the model prediction capability with long term dataset; (2) modify the original empirical model to account for the water pollution loads from each land cover classification; (3) examine and analyze the capability of this method for apportioning long term potential annual loads of water quality indicators such as total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and dissolved oxygen (DO) in river basins. The results of this study will be very useful to model the linkage between river water quality and land cover classes by separately considering the impact of each land cover type on the water pollution loads. 2. Materials and Methods 2.1. Collection of Long-Term Dataset In order to measure and assess changes in vegetation phenology and conditions as well as perform land cover type classification, this study uses 26 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images showing maximum annual normalized difference vegetation index (NDVI), at 1.1 km pixel resolution, across Japan for each year between 1984 and 2009. The NOAA AVHRR data was produced by the Sawada and Takeuchi Laboratory, Institute of Industrial Science, University of Tokyo. They were used to produce a land cover distribution map [28]. AVHRR data were geometrically corrected based on ground control point (GCP) matching by using PaNDA software, and registration error over the image was less than 1 pixel. PaNDA is a free software package for NOAA data analysis [29]. The AVHRR NDVI products were also provided by laboratory of Sawada and Takeuchi [28]. The NDVI can be calculated according to the following equation [30]: (r − r ) NDVI = 780 670 (1) (r780 + r670) where, r780 represents the near infrared band value for a cell, r670 represents the red band value for the cell. Daily NDVI was calculated using Band 1 (0.58–0.68 µm) and Band 2 (0.725–1.10 µm) Water 2016, 8, 361 3 of 14 images to produce the monthly maximum NDVI imagery, and by using the 12 monthly maximum NDVI imagery datasets, the effects of clouds on seasonal land cover changes can be removed to produce each yearly maximum images. All the processing was conducted by using the remote sensing softwareWater 2016, ERDAS8, 361 IMAGE (Version 9.2). The Iterative Self-Organizing Data Analysis3 of 13 Technique Algorithmimages (ISODATA) to produce [31 the,32 monthly], which maximum is an unsupervised NDVI imagery, classification, and by using the was 12 monthly used to maximum produce a yearly land coverNDVI classification imagery datasets, map the of effects Japan of using clouds theon seasonal 12 monthly land cover maximum changes can NDVI be removed imagery to datasets. Figure1 showsproduce an each example yearly maximum of the generated images. 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